Abstract: We will address two inferential aspects of noise multiplied magnitude microdata. First, in the context of disclosure risk assessment of tabular magnitude data, we study the consequences of noise multiplication when an intruder tries to speculate a target unit's value in a cell based on knowledge of the noise perturbed cell total and actual values of some units within the cell. Second, we discuss statistical methods to infer about a quantile of a microdata set based on their noise perturbed values. An application with income data will be presented.